Vertiefungsmodul Proteomics
In the core facility proteomics we perform protein identification, quantification and characterization of proteins, peptides, and post-translational modifications (PTMs).
sample origin
approach
label-free with DIA for standard samples
TMT-labeling for phosphoproteomics and other modifications
Chromatogram of one of the IP samples that were a subject of your previous exercise.
Chromatogram of a typical human complex sample.
Commonly we use MaxQuant or DiaNN platforms for spectra identification.
Tons of alternatives:
takes 5 minutes to open it in you favorite MS Excel like software
table contains 606934 rows
Our go-to is: R is a language and environment for statistical computing and graphics:
provides a wide variety of statistical techniques
Our go-to is: R, a language and environment for statistical computing and graphics:
provides a wide variety of statistical techniques
beautiful graphical output
Our go-to is: R is a language and environment for statistical computing and graphics:
provides a wide variety of statistical techniques
beautiful graphical output
highly extensible
This presentation is also written in coding language called Quarto, using reveal. js,an open source HTML presentation framework
Experimental design:
Similar to the Scaffold workflow:
Spectra identification software identified 5350 protein groups. After dropping 99 potential contaminants, 41 without replication (within subgroup), 5210 proteingroups were retained for further analysis.
Differential Expression Analysis quantifies whether condition differences are significant.
pvalue is a probability that the difference between groups arose due to random sampling.
There are two types of NAs:
systematic NAs: missing completely in some subgroups but detected in others (for at least half of the samples). These represent potential switch-like responses.
random NAs. They are missing in some samples, but the “missingness” is unrelated to subgroup. These samples do not require require imputation for statistical analysis to return pvalues.
In this dataset:
422 proteingroups have systematic NAs
2024 proteingroups have random NAs
2764 proteingroups have no NAs
GO stands for Gene Ontology and as the name suggests, it annotates genes using an ontology. It is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species.
Philipps–University Marburg
Department of Medicine
Biochemical/Pharmacological Centre, Building K|03
Karl–von–Frisch–Straße 2
35043 Marburg
GERMANY
50.8044, 8.80745
Philipps - Universität Marburg